The Effect of Measurement Subjectivity Classifications on Analysts' Use of Persistence Classifications When Forecasting Earnings Items

Earnings items are typically classified in financial reports based on their persistence and measurement subjectivity. Archival research examines investors' use of persistence and measurement subjectivity classifications for forecasting and valuation. However, this research typically examines only one of these classifications at a time and ignores the potential interactive implications of an earnings item's persistence and measurement subjectivity classifications. We recruited experienced financial analysts to participate in two experiments that examined the effect of measurement subjectivity classifications on analysts' use of persistence classifications when forecasting earnings items. We find that analysts rely less on an earnings item's persistence classification when measurement subjectivity is high relative to when measurement subjectivity is low. We also find that presentation format affects analysts' use of these two classifications. Specifically, we find that the matrix format (i.e., rows display persistence classifications and columns display measurement subjectivity classifications) facilitates analysts' combined use of persistence and measurement subjectivity classifications relative to the sequential format (i.e., the classifications are displayed separately). These findings suggest that archival research could improve its examination of market participants' use of earnings classifications for forecasting and valuation by recognizing that the implications of an earnings item's persistence classification can vary according to the item's measurement subjectivity classification. By also demonstrating how presentation format affects analysts' use of earnings classifications, our study provides further insights into this fundamental issue in accounting research and standard setting.